Enabling real-time information service on telehealth system over cloud-based big data platform

摘要

A telehealth system covers both clinical and nonclinical uses, which not only provides store-and-forward data services to be offline studied by relevant specialists, but also monitors the real-time physiological data through ubiquitous sensors to support remote telemedicine. However, the current telehealth systems do not consider the velocity and veracity of the big-data system in the medical context. Emergency events generate a large amount of the real-time data, which should be stored in the data center, and forwarded to remote hospitals. Furthermore, patients' information is scattered on the distributed data center, which cannot provide a high-efficient remote real-time service. In this paper, we proposes a probability'-based bandwidth model in a telehealth cloud system, which helps cloud broker to provide a high performance allocation of computing nodes and links. This brokering mechanism considers the location protocol of Personal Health Record (PHR) in cloud and schedules the real-time signals with a low information transfer between different hosts. The broker uses several bandwidth evaluating methods to predict the near future usage of bandwidth in a telehealth context. The simulation results show that our model is effective at determining the best performing service, and the inserted service validates the utility of our approach. (C) 2016 Elsevier B.V. All rights reserved.